Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations1001
Missing cells1001
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory284.8 KiB
Average record size in memory291.3 B

Variable types

Numeric9
Categorical4
Unsupported1

Alerts

Feedback is highly overall correlated with TextBlob SentimentHigh correlation
Overall Rating is highly overall correlated with Rating CategoryHigh correlation
Rating Category is highly overall correlated with Overall RatingHigh correlation
TextBlob Sentiment is highly overall correlated with FeedbackHigh correlation
Rating Category is highly imbalanced (59.7%)Imbalance
Vader Sentiment has 1001 (100.0%) missing valuesMissing
Student ID is uniformly distributedUniform
Student ID has unique valuesUnique
Vader Sentiment is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-10-02 17:13:40.562176
Analysis finished2025-10-02 17:13:53.583536
Duration13.02 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Student ID
Real number (ℝ)

Uniform  Unique 

Distinct1001
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500
Minimum0
Maximum1000
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:53.710277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median500
Q3750
95-th percentile950
Maximum1000
Range1000
Interquartile range (IQR)500

Descriptive statistics

Standard deviation289.10811
Coefficient of variation (CV)0.57821622
Kurtosis-1.2
Mean500
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83583.5
MonotonicityNot monotonic
2025-10-02T17:13:53.882877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7591
 
0.1%
3401
 
0.1%
2531
 
0.1%
6801
 
0.1%
8061
 
0.1%
6321
 
0.1%
8321
 
0.1%
7721
 
0.1%
9611
 
0.1%
51
 
0.1%
Other values (991)991
99.0%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
10001
0.1%
9991
0.1%
9981
0.1%
9971
0.1%
9961
0.1%
9951
0.1%
9941
0.1%
9931
0.1%
9921
0.1%
9911
0.1%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4975025
Minimum5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:53.987421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median8
Q39
95-th percentile10
Maximum10
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6929985
Coefficient of variation (CV)0.22580832
Kurtosis-1.2660868
Mean7.4975025
Median Absolute Deviation (MAD)1
Skewness-0.0266681
Sum7505
Variance2.8662438
MonotonicityNot monotonic
2025-10-02T17:13:54.078003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9182
18.2%
8177
17.7%
6172
17.2%
5165
16.5%
10153
15.3%
7152
15.2%
ValueCountFrequency (%)
5165
16.5%
6172
17.2%
7152
15.2%
8177
17.7%
9182
18.2%
10153
15.3%
ValueCountFrequency (%)
10153
15.3%
9182
18.2%
8177
17.7%
7152
15.2%
6172
17.2%
5165
16.5%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0819181
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:54.171664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median6
Q38
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5971682
Coefficient of variation (CV)0.42703111
Kurtosis-1.2344151
Mean6.0819181
Median Absolute Deviation (MAD)2
Skewness-0.063386839
Sum6088
Variance6.7452827
MonotonicityNot monotonic
2025-10-02T17:13:54.280674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9124
12.4%
6118
11.8%
2114
11.4%
7114
11.4%
10113
11.3%
8110
11.0%
4106
10.6%
3103
10.3%
599
9.9%
ValueCountFrequency (%)
2114
11.4%
3103
10.3%
4106
10.6%
599
9.9%
6118
11.8%
7114
11.4%
8110
11.0%
9124
12.4%
10113
11.3%
ValueCountFrequency (%)
10113
11.3%
9124
12.4%
8110
11.0%
7114
11.4%
6118
11.8%
599
9.9%
4106
10.6%
3103
10.3%
2114
11.4%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
4
218 
7
208 
6
196 
5
196 
8
183 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row8
3rd row6
4th row7
5th row8

Common Values

ValueCountFrequency (%)
4218
21.8%
7208
20.8%
6196
19.6%
5196
19.6%
8183
18.3%

Length

2025-10-02T17:13:54.392436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T17:13:54.489483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4218
21.8%
7208
20.8%
6196
19.6%
5196
19.6%
8183
18.3%

Most occurring characters

ValueCountFrequency (%)
4218
21.8%
7208
20.8%
6196
19.6%
5196
19.6%
8183
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4218
21.8%
7208
20.8%
6196
19.6%
5196
19.6%
8183
18.3%

Most occurring scripts

ValueCountFrequency (%)
Common1001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4218
21.8%
7208
20.8%
6196
19.6%
5196
19.6%
8183
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4218
21.8%
7208
20.8%
6196
19.6%
5196
19.6%
8183
18.3%
Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4305694
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:54.589941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8690459
Coefficient of variation (CV)0.52831401
Kurtosis-1.2300614
Mean5.4305694
Median Absolute Deviation (MAD)2
Skewness0.039309756
Sum5436
Variance8.2314246
MonotonicityNot monotonic
2025-10-02T17:13:54.678833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6112
11.2%
9107
10.7%
3107
10.7%
2105
10.5%
4100
10.0%
1100
10.0%
597
9.7%
895
9.5%
1093
9.3%
785
8.5%
ValueCountFrequency (%)
1100
10.0%
2105
10.5%
3107
10.7%
4100
10.0%
597
9.7%
6112
11.2%
785
8.5%
895
9.5%
9107
10.7%
1093
9.3%
ValueCountFrequency (%)
1093
9.3%
9107
10.7%
895
9.5%
785
8.5%
6112
11.2%
597
9.7%
4100
10.0%
3107
10.7%
2105
10.5%
1100
10.0%

Solves doubts willingly
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4745255
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:54.768044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8746479
Coefficient of variation (CV)0.52509535
Kurtosis-1.2213749
Mean5.4745255
Median Absolute Deviation (MAD)2
Skewness-0.010959603
Sum5480
Variance8.2636004
MonotonicityNot monotonic
2025-10-02T17:13:54.881459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6110
11.0%
2105
10.5%
1105
10.5%
8102
10.2%
9101
10.1%
4101
10.1%
797
9.7%
596
9.6%
1095
9.5%
389
8.9%
ValueCountFrequency (%)
1105
10.5%
2105
10.5%
389
8.9%
4101
10.1%
596
9.6%
6110
11.0%
797
9.7%
8102
10.2%
9101
10.1%
1095
9.5%
ValueCountFrequency (%)
1095
9.5%
9101
10.1%
8102
10.2%
797
9.7%
6110
11.0%
596
9.6%
4101
10.1%
389
8.9%
2105
10.5%
1105
10.5%

Structuring of the course
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6363636
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:54.970028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9202117
Coefficient of variation (CV)0.51810208
Kurtosis-1.2279027
Mean5.6363636
Median Absolute Deviation (MAD)3
Skewness-0.053877009
Sum5642
Variance8.5276364
MonotonicityNot monotonic
2025-10-02T17:13:55.513634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10123
12.3%
6109
10.9%
4103
10.3%
1102
10.2%
8100
10.0%
7100
10.0%
995
9.5%
294
9.4%
588
8.8%
387
8.7%
ValueCountFrequency (%)
1102
10.2%
294
9.4%
387
8.7%
4103
10.3%
588
8.8%
6109
10.9%
7100
10.0%
8100
10.0%
995
9.5%
10123
12.3%
ValueCountFrequency (%)
10123
12.3%
995
9.5%
8100
10.0%
7100
10.0%
6109
10.9%
588
8.8%
4103
10.3%
387
8.7%
294
9.4%
1102
10.2%
Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6623377
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:55.635475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8916898
Coefficient of variation (CV)0.51068834
Kurtosis-1.2439644
Mean5.6623377
Median Absolute Deviation (MAD)2
Skewness-0.089301775
Sum5668
Variance8.3618701
MonotonicityNot monotonic
2025-10-02T17:13:55.726099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7114
11.4%
8113
11.3%
10107
10.7%
9107
10.7%
4102
10.2%
3100
10.0%
198
9.8%
688
8.8%
287
8.7%
585
8.5%
ValueCountFrequency (%)
198
9.8%
287
8.7%
3100
10.0%
4102
10.2%
585
8.5%
688
8.8%
7114
11.4%
8113
11.3%
9107
10.7%
10107
10.7%
ValueCountFrequency (%)
10107
10.7%
9107
10.7%
8113
11.3%
7114
11.4%
688
8.8%
585
8.5%
4102
10.2%
3100
10.0%
287
8.7%
198
9.8%
Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5984016
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:55.811275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.886617
Coefficient of variation (CV)0.51561448
Kurtosis-1.2269209
Mean5.5984016
Median Absolute Deviation (MAD)2
Skewness-0.064492803
Sum5604
Variance8.3325574
MonotonicityNot monotonic
2025-10-02T17:13:55.920007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9111
11.1%
7106
10.6%
6104
10.4%
4103
10.3%
10102
10.2%
1102
10.2%
8100
10.0%
294
9.4%
590
9.0%
389
8.9%
ValueCountFrequency (%)
1102
10.2%
294
9.4%
389
8.9%
4103
10.3%
590
9.0%
6104
10.4%
7106
10.6%
8100
10.0%
9111
11.1%
10102
10.2%
ValueCountFrequency (%)
10102
10.2%
9111
11.1%
8100
10.0%
7106
10.6%
6104
10.4%
590
9.0%
4103
10.3%
389
8.9%
294
9.4%
1102
10.2%

Feedback
Categorical

High correlation 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
The teacher was very engaging and helpful!
119 
Great use of presentations, made concepts easier.
110 
Not recommended, workload too high.
107 
Loved the way doubts were solved quickly!
107 
The support was lacking for advanced topics.
100 
Other values (5)
458 

Length

Max length49
Median length45
Mean length42.472527
Min length35

Characters and Unicode

Total characters42515
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBest course I have attended so far!
2nd rowCourse structure was poor, needs improvement.
3rd rowThe support was lacking for advanced topics.
4th rowLoved the way doubts were solved quickly!
5th rowBest course I have attended so far!

Common Values

ValueCountFrequency (%)
The teacher was very engaging and helpful!119
11.9%
Great use of presentations, made concepts easier.110
11.0%
Not recommended, workload too high.107
10.7%
Loved the way doubts were solved quickly!107
10.7%
The support was lacking for advanced topics.100
10.0%
Very boring sessions, not interactive at all.96
9.6%
Course structure was poor, needs improvement.94
9.4%
Best course I have attended so far!94
9.4%
Excellent explanations, very clear and simple.91
9.1%
Assignments were too difficult and unclear.83
8.3%

Length

2025-10-02T17:13:56.102691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T17:13:56.588197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
the326
 
5.0%
was313
 
4.8%
very306
 
4.7%
and293
 
4.5%
not203
 
3.1%
too190
 
2.9%
were190
 
2.9%
course188
 
2.9%
teacher119
 
1.8%
engaging119
 
1.8%
Other values (43)4278
65.6%

Most occurring characters

ValueCountFrequency (%)
5524
13.0%
e4946
 
11.6%
a2724
 
6.4%
o2702
 
6.4%
s2656
 
6.2%
t2566
 
6.0%
r2383
 
5.6%
n2356
 
5.5%
i1662
 
3.9%
d1610
 
3.8%
Other values (28)13386
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34397
80.9%
Space Separator5524
 
13.0%
Other Punctuation1499
 
3.5%
Uppercase Letter1095
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4946
14.4%
a2724
 
7.9%
o2702
 
7.9%
s2656
 
7.7%
t2566
 
7.5%
r2383
 
6.9%
n2356
 
6.8%
i1662
 
4.8%
d1610
 
4.7%
c1485
 
4.3%
Other values (14)9307
27.1%
Uppercase Letter
ValueCountFrequency (%)
T219
20.0%
G110
10.0%
N107
9.8%
L107
9.8%
V96
8.8%
C94
8.6%
B94
8.6%
I94
8.6%
E91
8.3%
A83
 
7.6%
Other Punctuation
ValueCountFrequency (%)
.681
45.4%
,498
33.2%
!320
21.3%
Space Separator
ValueCountFrequency (%)
5524
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35492
83.5%
Common7023
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4946
13.9%
a2724
 
7.7%
o2702
 
7.6%
s2656
 
7.5%
t2566
 
7.2%
r2383
 
6.7%
n2356
 
6.6%
i1662
 
4.7%
d1610
 
4.5%
c1485
 
4.2%
Other values (24)10402
29.3%
Common
ValueCountFrequency (%)
5524
78.7%
.681
 
9.7%
,498
 
7.1%
!320
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII42515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5524
13.0%
e4946
 
11.6%
a2724
 
6.4%
o2702
 
6.4%
s2656
 
6.2%
t2566
 
6.0%
r2383
 
5.6%
n2356
 
5.5%
i1662
 
3.9%
d1610
 
3.8%
Other values (28)13386
31.5%

TextBlob Sentiment
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size55.8 KiB
Positive
728 
Negative
273 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8008
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive728
72.7%
Negative273
 
27.3%

Length

2025-10-02T17:13:56.846272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T17:13:56.970939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
positive728
72.7%
negative273
 
27.3%

Most occurring characters

ValueCountFrequency (%)
i1729
21.6%
e1274
15.9%
v1001
12.5%
t1001
12.5%
o728
9.1%
P728
9.1%
s728
9.1%
N273
 
3.4%
g273
 
3.4%
a273
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7007
87.5%
Uppercase Letter1001
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1729
24.7%
e1274
18.2%
v1001
14.3%
t1001
14.3%
o728
10.4%
s728
10.4%
g273
 
3.9%
a273
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
P728
72.7%
N273
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Latin8008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1729
21.6%
e1274
15.9%
v1001
12.5%
t1001
12.5%
o728
9.1%
P728
9.1%
s728
9.1%
N273
 
3.4%
g273
 
3.4%
a273
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII8008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1729
21.6%
e1274
15.9%
v1001
12.5%
t1001
12.5%
o728
9.1%
P728
9.1%
s728
9.1%
N273
 
3.4%
g273
 
3.4%
a273
 
3.4%

Vader Sentiment
Unsupported

Missing  Rejected  Unsupported 

Missing1001
Missing (%)100.0%
Memory size7.9 KiB

Overall Rating
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9154595
Minimum3.375
Maximum8.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-02T17:13:57.122607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.375
5-th percentile4.5
Q15.25
median5.875
Q36.625
95-th percentile7.375
Maximum8.25
Range4.875
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation0.89473398
Coefficient of variation (CV)0.1512535
Kurtosis-0.3033359
Mean5.9154595
Median Absolute Deviation (MAD)0.625
Skewness0.050467686
Sum5921.375
Variance0.80054889
MonotonicityNot monotonic
2025-10-02T17:13:58.520823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5.7574
 
7.4%
5.62565
 
6.5%
6.62556
 
5.6%
6.37552
 
5.2%
5.87548
 
4.8%
5.548
 
4.8%
5.2546
 
4.6%
543
 
4.3%
6.2543
 
4.3%
643
 
4.3%
Other values (30)483
48.3%
ValueCountFrequency (%)
3.3752
 
0.2%
3.51
 
0.1%
3.6252
 
0.2%
3.753
 
0.3%
3.8752
 
0.2%
46
 
0.6%
4.1259
 
0.9%
4.254
 
0.4%
4.37511
1.1%
4.524
2.4%
ValueCountFrequency (%)
8.253
 
0.3%
8.1254
 
0.4%
85
 
0.5%
7.8757
 
0.7%
7.755
 
0.5%
7.62512
1.2%
7.512
1.2%
7.37512
1.2%
7.2521
2.1%
7.12524
2.4%

Rating Category
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
Medium
860 
High
131 
Low
 
10

Length

Max length6
Median length6
Mean length5.7082917
Min length3

Characters and Unicode

Total characters5714
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowHigh

Common Values

ValueCountFrequency (%)
Medium860
85.9%
High131
 
13.1%
Low10
 
1.0%

Length

2025-10-02T17:13:58.708923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T17:13:58.825327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium860
85.9%
high131
 
13.1%
low10
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i991
17.3%
M860
15.1%
e860
15.1%
d860
15.1%
u860
15.1%
m860
15.1%
H131
 
2.3%
g131
 
2.3%
h131
 
2.3%
L10
 
0.2%
Other values (2)20
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4713
82.5%
Uppercase Letter1001
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i991
21.0%
e860
18.2%
d860
18.2%
u860
18.2%
m860
18.2%
g131
 
2.8%
h131
 
2.8%
o10
 
0.2%
w10
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
M860
85.9%
H131
 
13.1%
L10
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5714
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i991
17.3%
M860
15.1%
e860
15.1%
d860
15.1%
u860
15.1%
m860
15.1%
H131
 
2.3%
g131
 
2.3%
h131
 
2.3%
L10
 
0.2%
Other values (2)20
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i991
17.3%
M860
15.1%
e860
15.1%
d860
15.1%
u860
15.1%
m860
15.1%
H131
 
2.3%
g131
 
2.3%
h131
 
2.3%
L10
 
0.2%
Other values (2)20
 
0.4%

Interactions

2025-10-02T17:13:52.229187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:41.827294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T17:13:47.782161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:48.823209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:49.874696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:51.204089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:52.348223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:42.027404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:43.331325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:45.064816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:46.813510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:47.897121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:48.931946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:49.989367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:51.327200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:52.455628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:42.234439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:43.456287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:45.278001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:46.927273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:48.014207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:49.044118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T17:13:47.051050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:48.151569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:49.190001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T17:13:42.598145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:43.705261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:45.665994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T17:13:43.825650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:45.849339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:47.290372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T17:13:43.062626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:44.671634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:46.422546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:47.670155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:48.712421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:49.763689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:51.091624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T17:13:52.121796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-02T17:13:58.957415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Course recommendation based on relevanceDegree of difficulty of assignmentsExplains concepts in an understandable wayFeedbackOverall RatingProvides support for students going above and beyondRating CategorySolves doubts willinglyStructuring of the courseStudent IDTextBlob SentimentUse of presentationsWell versed with the subject
Course recommendation based on relevance1.000-0.0060.0070.0000.375-0.0320.186-0.000-0.010-0.0010.0170.000-0.013
Degree of difficulty of assignments-0.0061.0000.0300.0000.387-0.0070.1790.010-0.0530.0110.0000.000-0.007
Explains concepts in an understandable way0.0070.0301.0000.0000.366-0.0020.182-0.0260.0130.0150.0000.0300.010
Feedback0.0000.0000.0001.0000.0000.0000.0000.0420.0000.0000.9960.0300.016
Overall Rating0.3750.3870.3660.0001.0000.3670.8920.3970.3790.0220.0000.0790.198
Provides support for students going above and beyond-0.032-0.007-0.0020.0000.3671.0000.2030.004-0.0290.0400.0000.0170.031
Rating Category0.1860.1790.1820.0000.8920.2031.0000.1650.1800.0000.0000.1090.107
Solves doubts willingly-0.0000.010-0.0260.0420.3970.0040.1651.0000.0340.0080.0000.041-0.057
Structuring of the course-0.010-0.0530.0130.0000.379-0.0290.1800.0341.000-0.0500.0000.000-0.027
Student ID-0.0010.0110.0150.0000.0220.0400.0000.008-0.0501.0000.0420.0210.029
TextBlob Sentiment0.0170.0000.0000.9960.0000.0000.0000.0000.0000.0421.0000.0000.056
Use of presentations0.0000.0000.0300.0300.0790.0170.1090.0410.0000.0210.0001.0000.043
Well versed with the subject-0.013-0.0070.0100.0160.1980.0310.107-0.057-0.0270.0290.0560.0431.000

Missing values

2025-10-02T17:13:53.307650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-02T17:13:53.475161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Student IDWell versed with the subjectExplains concepts in an understandable wayUse of presentationsDegree of difficulty of assignmentsSolves doubts willinglyStructuring of the courseProvides support for students going above and beyondCourse recommendation based on relevanceFeedbackTextBlob SentimentVader SentimentOverall RatingRating Category
034052769218Best course I have attended so far!PositiveNaN5.000Medium
125365862129Course structure was poor, needs improvement.NegativeNaN4.875Medium
268077654231The support was lacking for advanced topics.PositiveNaN4.375Medium
380696715946Loved the way doubts were solved quickly!PositiveNaN5.875Medium
4632810846699Best course I have attended so far!PositiveNaN7.500High
583272783514Not recommended, workload too high.PositiveNaN4.625Medium
6772935210381Great use of presentations, made concepts easier.PositiveNaN5.125Medium
7961987443310Best course I have attended so far!PositiveNaN6.000Medium
881465864479The support was lacking for advanced topics.PositiveNaN6.125Medium
986359479445Loved the way doubts were solved quickly!PositiveNaN5.875Medium
Student IDWell versed with the subjectExplains concepts in an understandable wayUse of presentationsDegree of difficulty of assignmentsSolves doubts willinglyStructuring of the courseProvides support for students going above and beyondCourse recommendation based on relevanceFeedbackTextBlob SentimentVader SentimentOverall RatingRating Category
991678810527417Very boring sessions, not interactive at all.NegativeNaN5.500Medium
99254382493334Very boring sessions, not interactive at all.NegativeNaN4.500Medium
993908525321048The support was lacking for advanced topics.PositiveNaN4.875Medium
99458682498877Best course I have attended so far!PositiveNaN6.625Medium
9959899723384Not recommended, workload too high.PositiveNaN5.625Medium
9965587625779Not recommended, workload too high.PositiveNaN6.375Medium
99791355656761The support was lacking for advanced topics.PositiveNaN5.125Medium
99819995838112Assignments were too difficult and unclear.NegativeNaN4.625Medium
9995391027434101Excellent explanations, very clear and simple.PositiveNaN5.125Medium
100075972421599The teacher was very engaging and helpful!PositiveNaN4.875Medium